Articles | Volume 22, issue 4
https://doi.org/10.5194/nhess-22-1469-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/nhess-22-1469-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Machine-learning blends of geomorphic descriptors: value and limitations for flood hazard assessment across large floodplains
Andrea Magnini
CORRESPONDING AUTHOR
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Bologna, Italy
Michele Lombardi
Department of Computer Science and Engineering (DISI), University of Bologna, Bologna, Italy
Simone Persiano
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Bologna, Italy
Antonio Tirri
Leithà, Unipol Group, Milan and Bologna, Italy
Francesco Lo Conti
Leithà, Unipol Group, Milan and Bologna, Italy
Attilio Castellarin
Department of Civil, Chemical, Environmental and Materials Engineering (DICAM), University of Bologna, Bologna, Italy
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Short summary
We retrieve descriptors of the terrain morphology from a digital elevation model of a 105 km2 study area and blend them through decision tree models to map flood susceptibility and expected water depth. We investigate this approach with particular attention to (a) the comparison with a selected single-descriptor approach, (b) the goodness of decision trees, and (c) the performance of these models when applied to data-scarce regions. We find promising pathways for future research.
We retrieve descriptors of the terrain morphology from a digital elevation model of a 105 km2...
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